In this paper, we tackle the nowadays common problem of detecting outliers in LiDAR depth maps obtained by projecting the pointclouds over the image plane of an RGB camera. To deal with it, we have proposed an unsupervised framework trained solely on an RGB image plus the raw LiDAR depth map to estimate the LiDAR sensor confidence. Compared to existing methodologies
[1]}, [2]}, it yields state-of-the-art performance. Moreover, it constantly improves relevant tasks relying on LiDAR depth maps, such as semi-automatic annotation, depth completion [3]}, [4]}, guided stereo [5]} and sensor-guided optical flow [6]}.
Acknowledgment. This work was partially funded by University of Bologna and Ministero dello Sviluppo Economico (MISE) within the Proof of Concept 2020 program.
